TradeTech Blog


    Fri, 07 Sep 2012 11:05:49 GMT

    It’s a big question, and one the finance industry is struggling to answer; the thing in itself is valueless however (insert obvious pun) it’s what you do with it that counts.

    This much is obvious, but when it comes to proving that value, working out what each fragment, stream or supplier is worth requires constant measurement in a controlled, standardised and comparable way.

    This is no mean feat, and the best ways in which to get those results to make informed decisions about your data providers is one of the most contentious issues in financial data management right now.

    To try and uncover some of the truths, trends and practical steps to squeeze the most out of your data, The TradeTech Blog spoke to Hany Choueiri, Global Head – Entity Data Quality (GB&M) at HSBC.

    You will be discussing how to ‘measure the value of data every step of the way’:

    a. How is it possible to usefully measure the value of data and is this achievable before the end result of its usage been reached?

    The answer to this very much depends on the data in question. There are instances where the value of data can be measured and directly correlated to a time-framed business benefit and seamlessly form the basis of the business cases for data quality efforts. Examples of these are common in financial reporting where, for example, the credit ratings of clients can have a material impact on capital reserves.

    However, there are also many instances (as your question eludes to) where this correlation is not straightforward and the value is a longer term contributor to a strategic driver, a regulatory requirement, poses operational risks or even forms part of the business case that supports a change initiative. In many such cases, the Data Management Organization/Data Quality can agree a “data value formula” with business stakeholders that can be applied to estimate the ultimate value (which coincidentally can also be used to prioritize resources). This can, for example, include estimates of regulatory fines or simple probability techniques for operational risk scenarios. Once the value has been determined or estimated, simple time based value can be inferred by tracking the “% completion” of the population – similar to the “earned value” concept in project management.

    I would say the biggest challenge is in determining & agreeing the actual or estimated value of the data; once this is done, measuring the value at “every step of the way” is in my view the easier piece.

    Read the full Q&A at

    Related posts:

    1. Malcolm Chisholm on Data Management
    2. Regulation, Data & The DACH Region
    3. How is regulation affecting data management in Germany, Austria and Switzerland?

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